Recent advances in deep learning for protein-protein interaction: a review.

Journal: BioData mining
Published Date: Jun 16, 2025

Abstract

Deep learning, a cornerstone of artificial intelligence, is driving rapid advancements in computational biology. Protein-protein interactions (PPIs) are fundamental regulators of biological functions. With the inclusion of deep learning in PPI research, the field is undergoing transformative changes. Therefore, there is an urgent need for a comprehensive review and assessment of recent developments to improve analytical methods and open up a wider range of biomedical applications. This review meticulously assesses deep learning progress in PPI prediction from 2021 to 2025. We evaluate core architectures (GNNs, CNNs, RNNs) and pioneering approaches-attention-driven Transformers, multi-task frameworks, multimodal integration of sequence and structural data, transfer learning via BERT and ESM, and autoencoders for interaction characterization. Moreover, we examined enhanced algorithms for dealing with data imbalances, variations, and high-dimensional feature sparsity, as well as industry challenges (including shifting protein interactions, interactions with non-model organisms, and rare or unannotated protein interactions), and offered perspectives on the future of the field. In summary, this review systematically summarizes the latest advances and existing challenges in deep learning in the field of protein interaction analysis, providing a valuable reference for researchers in the fields of computational biology and deep learning.

Authors

  • Jiafu Cui
    Inner Mongolia International Mongolian Hospital, Hohhot, 010065, China.
  • Siqi Yang
    College of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang, 110142, China.
  • Litai Yi
    State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, College of Life Sciences, Inner Mongolia University, Hohhot, 010021, China.
  • Qilemuge Xi
    The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China.
  • Dezhi Yang
    Robotics Institute, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
  • Yongchun Zuo
    The State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals